Visualization, sharing and analysis of large data sets

ABSTRACT

Systems and methods for visualization, sharing and analysis of large data sets are described. Systems and methods may include receiving an input data set, wherein the input data set includes data that can be classified in classification dimensions wherein a first classification dimension is a linear ordering of data entries and a second classification dimension represents analysis criteria, traits of the data entries, or aspects of the data entries; obtaining an unabridged data table listing results for each combination of coordinates in the first classification dimension and the second classification dimension; and displaying contents of the unabridged data table as a visual array wherein two axes correspond to the coordinates and a third axis corresponds to a third classification dimension, wherein the third classification dimension represents an actual value of the respective data point for the coordinates. Methods may also assess the visual array, such as by identifying one or more regions of high density of signals.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present invention claims priority to U.S. Provisional PatentApplication No. 61/897,524, filed Oct. 30, 2013, the contents of whichare incorporated by reference herein in their entirety.

FIELD OF THE INVENTION

The present invention relates to systems and methods for visualization,sharing and analysis of large data sets, and, more specifically, tosystems and methods for data that can be classified in two dimensionsfor grouping into non-overlapping subsets.

BACKGROUND OF THE INVENTION

Many large data sets are difficult to work with and a challenge toanalyze successfully. In particular, it is often difficult to quicklydetermine relevant data or associations of interest over a large dataset. This problem arises in a number of fields.

For example, as genome-wide association studies (GWAS) plus whole genomesequence (WGS) analyses for complex human disease determinants areexpanding, it is increasingly necessary to develop strategies tofacilitate large data sharing, rapid replication and validation ofpotential disease-gene associations. This is especially true for signalsthat straddle the threshold for genome-wide significance due to smalleffect size, lack of statistical power in the study population, or acombination of these.

Annotations of human genome variation have identified some 60 millionsingle nucleotide polymorphisms (SNPs), which offer the promise ofconnecting nucleotide and structural variation to hereditary traits.Genotyping arrays that resolve millions of common SNPs have enabled over2000 GWAS to discover principal genetic determinants of complexmultifactorial human diseases. Today, whole genome sequence associationhas extended the prospects for personalized genomic medicine, capturingrare variants, copy number variation, indels, epistatic and epigeneticinteractions in hopes of achieving individualized genomic assessment,diagnostics, and therapy of complex maladies by interpreting one'sgenomic heritage.

GWAS studies to date have produced conflicting signals because many SNPassociations fail to replicate in independent studies. Further, GWASfrequently fail to implicate previously-validated gene regions describedin candidate gene associations for the same disease, and in most casesoffer less than 10% of the explanatory variance for the diseaseetiology. In addition, discovered gene variants are frequently nested innoncoding desert regions of the genome that are difficult to interpret.At least part of these weaknesses derive from discounting SNPassociation “hits” that fail to achieve “genome-wide significance”, awidely accepted, albeit conservative, statistical threshold set todiscard the plethora of seemingly false positive statisticalassociations (Type I errors) that derive from the large number of SNPsinterrogated.

A challenge to genetic epidemiology involves disentangling the truefunctional associations that fall below the genome-wide significancethreshold from the myriad of statistical artifacts that also occur. Noone has developed a real solution to this conundrum though someapproaches have been offered. Many researchers agree that more widelypracticed open access data sharing of unabridged GWAS data would offerthe opportunity for multiple plausible approaches to bear on thisquestion. However, for many cohorts, especially those developed beforethe advent of the genomics era, participants were not consented for openaccess of genome-wide data. Since patient anonymization is virtuallyimpossible with genetic epidemiological data, the prospects of sharingpatients' genotype and clinical data may conflict with ethical concernsover protecting the individual privacy of study subjects.

Needs exist for improved systems and methods for visualization, sharingand analysis of large data sets.

SUMMARY OF THE INVENTION

Certain embodiments may provide systems and methods for visualization,sharing, and/or analysis of large data sets. Certain embodiments mayautomate and facilitate genetic epidemiological discovery. This may beaccomplished by:

1) Automated Analysis: Rapid gene association search and discoveryanalysis of large genome-wide datasets;

2) Display: Expanded visual display of gene associations for genome-widevariants (SNPs, indels, CNVs) including Manhattan plots, 2D and 3Dsnapshots of any gene region, and a dynamic genome browser illustratinggene association chromosomal regions;

3) Replication: Real time validation/replication of candidate orputative genes suggested from other sources, limiting GWAS statisticalpenalties related to Bonferroni multiple-testing correction; and

4) Release: Open data release and sharing by eliminating privacyconstraints (IRB, Informed consent, HIPAA etc.) on unabridged results,which allows for open access comparative and meta-analysis.

In certain embodiments, systems and methods may be provided forvisualization, replication, sharing or analysis of large data sets.Systems and methods may include receiving an input data set, wherein theinput data set comprises data that can be classified in classificationdimensions wherein a first classification dimension is a linear orderingof data entries and a second classification dimension representsanalysis criteria, traits of the data entries, or aspects of the dataentries; obtaining an unabridged data table listing results for eachcombination of coordinates in the first classification dimension and thesecond classification dimension; and displaying contents of theunabridged data table as a visual array wherein two axes correspond tothe coordinates and a third axis corresponds to a third dimension,wherein the third dimension represents an actual value of the respectivedata point for the coordinates. Classification dimensions may define aplane. Additional dimensions may represent dimensions other than thosedefining the plane.

In certain embodiments, the visual array may include a fourth dimensionthat represents an additional component of the data point value. Thefourth classification dimension may be represented by color or shape ofthe bars in the three-dimensional visual array.

In certain embodiments, the visual array may be a three-dimensionalmoving video mode above a surface wherein two axes in the horizontalplane represent the first classification dimension and the secondclassification dimension, while height of blocks rising from that planerepresent the third dimension.

The input data set may be ready for output (i.e. can be classified intwo dimensions as it is), and further comprising optional reformattingof the input data set to create the unabridged data table. Certainembodiments may also include analyzing the input data set or convertingthe input data set to obtain the unabridged data table.

In certain embodiments, the input data set may be the result of agenome-wide association studies (GWAS) or whole genome sequence (WGS)association study or its analysis. A first axis of the visual array mayrepresent SNPs linearly ordered according to their genomic positions, asecond axis may represent different association tests performed, heightof blocks rising above the surface represents reversed sign decimallogarithm of p-value (−log p-value), and color represents quantitativeassociation statistics (QAS). The quantitative association statisticsmay represent direction and strength of associations for associationtests and may be selected from the group consisting of: odds ratio,relative hazard, ez2-transformed correlation coefficient, andcombinations thereof. In certain embodiments, an output contains one ormore of the following features: data table listing all SNPs, comprisingnames and chromosome coordinates, with p-values and QASs for each test;Manhattan plots showing p-values for all genotyped SNPs for any singletest, computed for all tests; three-dimensional moving video modeallowing navigation above the chromosome surface and viewing moredetailed association statistics and SNP information; two-dimensionalsnapshot of a selected genomic region in the form of a heat plot indexedby the p-values; three-dimensional snapshot of a selected genomic regionin the form of a static image of a three-dimensional moving video mode;polarized three-dimensional snapshot of a selected genomic region wherevalues of QAS are inverted according to the value of linkagedisequilibrium (LD) between minor allele of the index SNP and those ofother neighboring SNPs; summary of all test results for a single SNP;analytical report of all tests performed for a single SNP; and list ofSNP-test combinations ranked according to p-values, QASs or density ofsignificant p-values across closely linked SNPs and related tests for adisease stage.

Certain embodiments may include receiving a search query regarding thevisual array to locate any single region of interest by SNP rs-number,gene name, chromosome coordinates or the threshold for −log p-valuewithin the currently selected chromosome or throughout the whole genomeand displaying this region of interest. The analysis may utilize genomicdata in the form of patients' genotypes and clinical (phenotypic) dataselected from the group consisting of, but not limited to: categoricalclinical data, right-censored survival clinical data, and combinationsthereof. If the primary data (genotypes and clinical/phenotypic data)are received, rather than processed data (p-values and QAS), theanalysis of these primary data may include tests selected from the groupconsisting of, but not limited to: categorical tests, proportionalhazards survival tests, categorical tests for survival data,Hardy-Weinberg equilibrium tests, and combinations thereof.

To better recognize signals in the dataset of study and distinguish themfrom the noise, additional analysis may be performed that may includeassessment of the shape of a three-dimensional surface landscape in thevisual array. The assessment may include identification of regions ofhigh or low density of statistical signals.

Certain embodiments may include performing meta-analyses of the visualarray in a dynamic browser displaying association results.

Certain embodiments may include anonymization of a genomic data set toproduce the input data set. Embodiments may also include isolating andsampling DNA to produce the genomic data set.

Certain embodiments may include systems and methods for visualization,replication, sharing and analysis of large data sets. Systems andmethods may include accessing an input data set, wherein the data set(or the result of its analysis) comprises data that can be classified intwo dimensions wherein a first dimension is a linear ordering of dataentries and a second dimension represents analysis criteria, traits ofthe data entries, or aspects of the data entries; if needed, analyzingthe data set or converting it to obtain an unabridged data table,wherein the data table lists results for each combination of thecoordinates in the two dimensions (classification dimensions); anddisplaying contents of the unabridged data table as a visual array wherefirst two axes correspond to the coordinates in the two classificationdimensions and the third one corresponds to the actual value (result) ofthe respective data point for these coordinates; optionally, the fourth“dimension” (e.g. color or shape) can be introduced to representadditional component of the data point value.

In certain embodiments, the visual array may be a three-dimensionalmoving video mode above a surface wherein two axes in the horizontalplane represent classification dimensions, while height, and optionallycolor, of blocks rising from that plane represent value for thecombinations of coordinates in classification dimensions. The input dataset may be ready for output (can itself be classified in two dimensions)and the unabridged data table can be obtained by the optionalreformatting of this data set. Alternatively, the input data set mayfirst need to be analyzed to obtain the unabridged data table that canbe output. The input data set may be the result of the GWAS or WGSassociation study or its analysis. A long axis of the visual array mayrepresent SNPs linearly ordered according to their genomic positions. Ashort axis may represent different association tests performed. Heightof blocks rising above the surface may represent reversed sign decimallogarithm of p-value (−log p-value) and color may represent quantitativeassociation statistics (QAS). The QAS may represent and/or explaindirection and strength of associations for association tests and may beselected from the group consisting of, but not limited to, thefollowing: odds ratio, relative hazard, ez2-transformed correlationcoefficient, and combinations thereof. The output may contain any of thefollowing features or their combinations: data table listing all SNPs(names and chromosome coordinates) with p-values and QASs for each test;Manhattan plots showing p-values for all genotyped SNPs for any singletest, computed for all tests; three-dimensional dynamic HIGHWAY browseras described herein, allowing navigation above the chromosome surfaceand viewing more detailed association statistics (p-value, QAS) as wellas SNP information (rs-number, coordinates, minor allele frequency—MAF,etc.) by placing the mouse pointer atop the bar for any SNP-testcombination or by any other way; two-dimensional snapshot (2D-SNAPSHOT)of a selected genomic region in the form of a heat plot indexed by thep-values; three-dimensional snapshot (3D-SNAPSHOT) of a selected genomicregion in the form of a visual array as described herein, onlynon-dynamic, that may be labeled with SNP rs-number, chromosomecoordinates and minor allele frequencies; polarized three-dimensionalsnapshot of a selected genomic region which is identical to the3D-SNAPSHOT, except that values of QAS are inverted according to thevalue of linkage disequilibrium (LD) between minor allele of the indexSNP and those of other neighboring SNPs; summary of all test results fora single SNP (TRAX PAGE); detailed analytical report of all testsperformed for a single SNP (TRAX PAGE) including tables, bar graphs,survival curves and additional parameters for each test; and list ofSNP-test combinations ranked according to p-values, QASs or density ofsignificant p-values across closely linked SNPs and related tests for adisease stage, providing an association discovery tool. Certainembodiments may include receiving a search query regarding the visualarray to locate any single region of interest by SNP rs-number, genename, chromosome coordinates or the threshold for −log p-value withinthe currently selected chromosome or throughout the whole genome anddisplaying this region of interest. The analysis may utilize genomicdata in the form of patients' genotypes and clinical (phenotypic) dataselected from, but not limited to, categorical clinical data,right-censored survival clinical data, and combinations thereof. If theprimary data (genotypes and clinical/phenotypic data) are received,rather than processed data (p-values and QAS), the analysis of theseprimary data may include tests selected from the group consisting of,but not limited to, the following: categorical tests, proportionalhazards survival tests, categorical tests for survival data,Hardy-Weinberg equilibrium tests, and combinations thereof. To betterrecognize signals in the dataset of study and distinguish them from thenoise, additional analysis may be performed that can include assessmentof the shape of three-dimensional surface landscape in the visual arraythrough various methods including identification of the regions of highor low density of statistical signals.

Systems and methods for visualization, sharing and analysis of largedata sets may include: accessing an input data set, wherein the data set(or the result of its analysis) comprises data that can be classified intwo dimensions wherein a first dimension is a linear ordering of dataentries and a second dimension represents analysis criteria, traits ofthe data entries, or aspects of the data entries; if needed, analyzingthe data set or converting it to obtain an unabridged data table,wherein the data table lists results for each combination of thecoordinates in the two dimensions (classification dimensions); anddisplaying contents of the unabridged data table as a visual array wherefirst two axes correspond to the coordinates in the two classificationdimensions and the third one corresponds to the actual value (result) ofthe respective data point for these coordinates; optionally, the fourth“dimension” (e.g. color or shape) can be introduced to representadditional component of the data point value.

Systems and methods for visualization, sharing and analysis of largedata sets may include optionally anonymization of a genomic data set toproduce a primary data set; accessing the primary data set; performingstatistical tests on the primary data set and arranging obtainedderivative data set (analysis results) in a first dimension and a seconddimension wherein a first dimension is a linear ordering of dataentries; displaying contents of the derivative data set as a visualarray; sharing the derivative dataset as visual array, data tables,other visual displays, etc.; and performing meta-analyses of the visualarray in a dynamic browser displaying association results. Certainembodiments may include isolating and sampling DNA to produce thegenomic data set. A large data set may be a genome-wide associationstudies (GWAS) or a whole genome sequence (WGS) analysis.

Certain embodiments may include systems and/or methods of displayinglarge data sets. The systems and/or methods may include receiving aninput data set, wherein the input data set comprises data that can beclassified in two classification dimensions; displaying the input dataset in a graph that have three or more output dimensions; and allowing auser to navigate above a plane of two output dimensions from the graph.

In certain embodiments, the three or more output dimensions may be fouroutput dimensions. The navigation may be a three-dimensional movingvideo mode. The plane may be a representation of a chromosome, whereinthe user may view association statistics and SNP information. A baselinetransversal axis of the graph may be a list of tests, and a longitudinalaxis of the graph may list ordered SNPs to create a surface. A verticalaxis rising out of the surface may represent −log p-values, and colormay reflect quantitative association statistics. A large data set may bea genome-wide association studies (GWAS) or a whole genome sequence(WGS) analysis.

Additional features, advantages, and embodiments of the invention areset forth or apparent from consideration of the following detaileddescription, drawings and claims. Moreover, it is to be understood thatboth the foregoing summary of the invention and the following detaileddescription are exemplary and intended to provide further explanationwithout limiting the scope of the invention as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the invention and are incorporated in and constitute apart of this specification, illustrate preferred embodiments of theinvention and together with the detailed description serve to explainthe principles of the invention. In the drawings:

FIG. 1 shows an exemplary system for gene discovery and data sharing indisease association analyses across the genome.

FIG. 2 shows an exemplary system for computational aspects of genediscovery and data sharing in disease association analyses across thegenome.

FIG. 3 shows exemplary MANHATTAN plot for selected association testillustrating p-values of association for all genotyped SNPs.

FIG. 4 shows an exemplary HIGHWAY browser view, a dynamic 3D chromosomeview of associated gene data for selected region illustratingsignificant p-values (block height), quantitative association statistics(QAS)-based direction (color: green for QAS<1.0, red for QAS>1.0) andQAS-based strength (color intensity) of association for linked SNPsalleles.

FIGS. 5A-5L shows an exemplary 2D-SNAPSHOT heat plot of associated genedata for selected region illustrating significant p-values (colorintensity) for association of linked SNP alleles.

FIGS. 6A-6P shows an exemplary 3D-SNAPSHOT of selected regionillustrating significant p-values (block height), QAS-based direction(color: green for QAS<1.0, red for QAS>1.0) and QAS-based strength(color intensity) of association for linked SNP alleles.

FIGS. 7A-7P shows an exemplary POLARIZED 3D-SNAPSHOT of associated genedata for selected region illustrating significant p-values (blockheight), QAS-based direction (color: green for QAS<1.0, red for QAS>1.0)and QAS-based strength (color intensity) of association for linked SNPalleles.

FIGS. 8A-8B shows an exemplary TRAX PAGE, presenting a summary of alltest results for a single SNP for a study group (e.g., p-values and QASsfor HIV infection, AIDS progression using categorical and survivaltests, AIDS sequelae, and HAART outcomes can be viewed and compared).

FIGS. 9A-9B shows an exemplary section of a detailed TRAX REPORT ofderived statistics for all the tests accomplished for a single SNPillustrating an example of the appearance of the production of survivalcurves.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Systems and methods are described for using various tools and proceduresfor visualization, sharing and analysis of large data sets. In certainembodiments, the tools and procedures may be used in conjunction withgenome studies. The examples described herein relate to human genomestudies for illustrative purposes only. The systems and methodsdescribed herein may be used for many different industries and purposes,including human, animal, plant, bacteria and other genome studies and/orother industries completely. In particular, the systems and methods maybe used for any industry or purpose where large data analysis andsharing is needed. For multi-step processes or methods, steps may beperformed by one or more different parties, servers, processors, etc.

Large datasets may be any data sets. In certain embodiments, large datasets may be genome-wide association studies (GWAS) and/or whole genomesequence (WGS) analyses. In certain embodiments, large data sets may beany data set with more than approximately 100 data points, more thanapproximately 1,000 data points, more than approximately 10,000 datapoints, more than approximately 100,000 data points, more than 1,000,000data points, more than approximately 10,000,000 data points, more than100,000,000 data points, etc. Data points may be ordered to create anaxis of a display. The display may be sequential or another type oforder. The display may have one axis corresponding to the number, name,location, etc. of the data points. This axis may be one of a pluralityof classification dimensions for the data set. The display may be amoving graph in two or more dimensions. The movement may be along thisaxis corresponding to the number, name, location, etc. of the datapoints.

Embodiments described herein may be applicable to visualization,analysis and sharing of any data that can be classified in twodimensions, one of which may serve as a reference for linear ordering ofdata entries, while another may represent different analysis criteria,different traits or aspects of data and thus allow grouping of thesedata into several non-overlapping subsets. In an extreme case, thesecond dimension can consist of a single unit, thus grouping all datatogether. If the input data can be classified in this two-dimensionalsetting, they then can be output and visualized using the approachdescribed herein. In certain embodiments, they may be output andvisualized as a dynamic three-dimensional graphical view shown in anexemplary visualization system where first two axes correspond to twoclassification dimensions and a third axis may be an actual value of therespective data point (e.g. p-value or some other quantitative orqualitative result) corresponding to the combination of the values oftwo classification parameters. Optionally, a fourth “dimension” (e.g.color or shape) can be introduced to represent an additional componentof the data point value. In addition to visual inspection, automaticassessment of the shape and structure of this three-dimensionalrepresentation can be used for analysis of the data. This can be done bylooking for regions of high and/or low density of statistical signalsthrough the study of three-dimensional surface “landscape” produced in agraphical representation, which is demonstrated in an exemplaryrealization herein as a density feature. This analysis approach can behelpful to improve over existing techniques for recognition of truesignals and distinguishing them from noise.

Certain embodiments may provide for meta-analysis. Meta-analysis may bestatistical methods for contrasting and combining results from differentstudies to identify patterns among study results, sources ofdisagreement among those results, or other interesting relationships.

To clarify and illustrate the two-dimensional classification of inputdata and three-dimensional output format, an exemplary approachdescribed herein relates to the analysis of the data from GWAS and WGSassociation studies. In this case, the first dimension, which allowslinear ordering of data entries, may be the genomic position of the SNP,with the SNPs being naturally ordered along the chromosome. The seconddimension in this case may be different association tests performed (fordifferent stages of disease, patient subgroups, genetic models and soon) that allows grouping data in several subsets where each subgroup isthe collection of results for all SNPs for the same one test. This mayallow for representation of input data with SNPs on one axis, tests onanother axis and the results (reverse sign decimal logarithm of p-value)of the given test for the given SNP on the third axis. An additional“dimension”, such as color, may also be used to represent quantitativeassociation statistics (QAS, see below). Therefore, a three-dimensionaldynamic output may be provided.

In the described exemplary embodiment the data that are output in theabove format are not the primary data (genotypes and phenotypic/clinicaldata of patients/study participants), but rather the derivative resultsof analysis (p-values and QAS), represented in the dynamic 3Dvisualization by height of the bars and their color, respectively). Suchoutput of the test/analysis results and not the original (primary or“raw”) data is not required by the described invention in general, butcan be beneficial since it can facilitate data sharing in two aspects(but not limited by these examples): 1) in cases where open release oforiginal (primary) data can be complicated due to privacy,confidentiality, commercial secret, copyright, ethical or otherconcerns, release of derivative test/analysis results may still allowfor data sharing, thus overcoming these obstacles; and/or 2) in caseswhere release of original (raw) data can be complicated due to the largesize of the data, derivative test/analysis results may be smaller due totheir summary nature, which may allow for effective data sharing. Inaddition to the advantages described above, representation of data inthis format may also be easier to understand and comprehend, and allowfor a more intuitive organization of the data providing grounds foradequate cataloguing and thus effective storage and retrieval of theinformation.

As stated above, this representation, visualization, analysis and datasharing approach may be applicable to any data that can be laid out intwo dimensions with results forming the third dimension. This can beespecially important and relevant, among other things, for largedatasets, such as large-scale biological data. One area for applicationmay be analysis of data that correlates with the genomic position. Apartfrom the realization described below for the human GWAS and WGSassociation studies data analysis, the following applications and/orrealizations can be envisioned without limiting the scope of theinvention:

-   -   representation, visualization, analysis, cataloguing,        replicating and/or sharing of data from the studies of human        transcriptome, such as (but not limited to) RNA-seq studies for        association with phenotypic traits, disease predisposition,        clinical symptoms etc.;    -   representation, visualization, analysis, cataloguing,        replicating and/or sharing of data from the studies of human        proteome, such as (but not limited to) proteomic studies for        association with phenotypic traits, disease predisposition,        clinical symptoms etc.;    -   representation, visualization, analysis, cataloguing,        replicating and/or sharing of data from the studies of human        epigenome, such as (but not limited to) methylome studies for        association with phenotypic traits, disease predisposition,        clinical symptoms etc.;    -   representation, visualization, analysis, cataloguing,        replicating and/or sharing of data from the studies of human        metabolome, such as (but not limited to) small-molecule        metabolite studies for association with phenotypic traits,        disease predisposition, clinical symptoms etc.;    -   representation, visualization, analysis, cataloguing,        replicating and/or sharing of data from the studies involving        personalized human genome and/or transcriptome and/or proteome        and/or methylome and/or metabolome assessment and        interpretation, such as (but not limited to) assessment of        individual genome composition by whole genome sequencing for        diagnostic purposes and/or for following estimation of personal        health risks in relation to lifestyle, employment, health and        life insurance etc. by comparison of obtained personal genomic        data with known genetic associations and/or genomic risk        factors;    -   any of the above applications performed in non-human species,        such as (but not limited to) GWAS or WGS association studies in        mice, rats, cats, dogs, chickens, cows, horses and others,        studies for association of genome and/or transcriptome and/or        proteome and/or metabolome with infectivity, pathogenicity and        immunogenicity in viruses and bacteria, including vaccine        studies;    -   representation, visualization, analysis, cataloguing,        replicating and/or sharing of data from pharmacogenomics and        pharmacogenetics studies and clinical trial studies in humans        and non-human species, such as (but not limited to) studies of        the patients' response to the medication in relation to their        genetic background;    -   representation, visualization, analysis, cataloguing,        replicating and/or sharing of data from the high-throughput        discovery of the biological drugs (e.g., therapeutic monoclonal        antibodies) which relate properties of the molecules to their        nucleotide and/or amino acid sequence, such as (but not limited        to) studies where data can be plotted as genomic position versus        various tests for different properties of the molecule, such as        affinity for different targets, thermal stability, biological        activity, toxicity etc.;    -   representation, visualization, analysis, cataloguing,        replicating and/or sharing of data from the high-throughput        discovery of the small-molecule drugs which relate properties of        the molecules to their chemical structure and/or concentration,        such as (but not limited to) studies where data can be plotted        as chemical structure versus various tests for different        properties of the molecule (such as affinity, biological        activity, toxicity etc.) and/or for different concentrations;    -   representation, visualization, analysis, cataloguing,        replicating and/or sharing of data from the studies of the        genetic polymorphism, such as (but not limited to) data from the        studies of the genetic polymorphism between vaccine batches        and/or natural isolates of the pathogen, plotted as genomic        position versus abundance of each of the four nucleotides in the        given genomic position.

In relation to the realization described herein, potential purposes,applications and advantages may include one or more of the following:

-   -   automated gene association detection in large association        studies for complex human diseases;    -   assessment of candidate genes that are suggested by functional        studies for a disease by inspecting SNPs or variants in        candidate gene chromosomal regions. This inspection may abrogate        significant statistical multiple-testing penalties of GWAS or        WGS analyses because it is hypothesis driven;    -   real-time independent replication of GWAS and WGS association        hits from other cohorts run for the same disease;    -   providing an analysis platform for interpretation of multiple        cohort studies together, the principal of a meta-analysis;    -   providing a new approach for open access/unabridged data release        of gene association studies results without violating privacy,        IRB (institutional review board) regulations, HIPAA (The Health        Insurance Portability and Accountability Act of 1996) provisions        or informed consent constraints. Although the released results        are dependent on private clinical and genetic personal data,        these data are not released, and the released results are the        derivative ones (p-values, QAS, detailed survival curves, bar        plots and tables in TRAX PAGE and TRAX REPORTS);    -   applicable to GWAS, to whole genome sequence association        studies, and personalized genome sequence analysis and        diagnostics.

The following is an exemplary use of certain embodiments for genomeanalysis. An exemplary data release platform may display unabridgedgenetic association data results without compromising privacy orinformed consent constrictions, allowing for rapid discovery andreplication opportunities. An exemplary dataset that is released via thedescribed embodiment is a result of a survey for HIV-AIDS resistancegenes screened in a large multicenter cohort genome-wide associationstudy.

An exemplary web-based, server-based or hardware-based analyticalpackage may address issues with previous approaches to genome studieswith an organized open release of unabridged SNP-test associationresults from GWAS and whole genome sequencing (WGS) association studies.As illustrated herein for an exemplary embodiment, a Genome-WideAssociation Track Chromosome Highway (GWATCH) may be a dynamic genomebrowser that displays primary analysis results—p-values and QAS—frommultiple association tests performed for one or more cohorts in a GWASor WGS association study as a visual array ordered by SNP chromosomalposition.

As a first step in analysis, GWATCH may allow scrolling across anychromosomal region to view results (p-values and QAS) of severalthousand disease-SNP association tests in any chromosome region housinga gene of interest. The imagery provides a dynamic traverse along ahuman chromosome producing a “bird's eye” view of the strong SNPassociations that rise above the chromosome highway surface. The idea isto visualize association results across a gene region (e.g. one that mayinclude a highly significant SNP association) for all the testsperformed (on the same or different cohorts) and for all theneighboring, potentially proxy SNPs (i.e., SNPs which track theneighboring causal, disease-affecting SNP due to the linkagedisequilibrium, LD) for the same tests. The view may allow visualinspection of related associations for non-independent tests, andnon-independent SNP genotypes (e.g., analysis of tests for the samedisease stage and/or for the group of nearby SNPs).

Typical input of a GWAS analysis may include a large unabridgedData-Table listing p-values and QASs across multiple SNP associationtests performed for a list of a million or more ordered SNPs. GWATCH maydisplay the Data-Table, Manhattan plots for each test (FIG. 3), adynamic chromosome browser that indicates significant p-values and QASsfrom the Data-Table (FIG. 4) or produces analytical result reports forany SNP (TRAX PAGE and TRAX REPORT in Table 1 and FIGS. 8A-8B and FIGS.9A-9B).

Described herein as an exemplary use, is an application of GWATCH usinga GWAS carried out with study participants enrolled in eight prospectiveHIV-AIDS cohorts, searching for AIDS Restriction Genes-ARG. In thiscase, a GWAS meta-analysis was performed on 5922 patients withdistinctive clinical outcomes genotyped using an Affymetrix 6.0genotyping array (700,022 SNPs after quality control—QC—filters) andparsed into three population groups (Table 2):

Group A) A select group of 1527 European American individuals;

Group B) A total of 4462 European American individuals that includesGroup A;

Group C) An independent group of 1460 African American individuals.

Based upon available clinical information, 123 genome wide association(GWA) experimental tests were performed on Group A, 144 GWA tests onGroup B, and 60 GWA tests on Group C (Table 3). The GWA tests includeallele and genotype associations for HIV acquisition/infection, AIDSprogression (including categorical and survival analyses), AIDS definingconditions and Highly Active Antiretroviral Therapy (HAART) outcomes.The unabridged dataset displayed in GWATCH-ARG, however, is far richerthan those analyzed in the previous studies. GWATCH-ARG presentscomplete results for 700,022 SNPs for 327 tests (Table 3) for 5922 studyparticipants listed in Table 2.

For the three ARG analysis groups A-C (Table 2), GWATCH-ARG presentsseveral distinctive display features that allow detailed inspection ofthe composite GWAS results for individual SNPs and for groups of linkedSNPs across all human chromosomal regions (Table 1).

1.) The principal data set is a large DATA TABLE listing all SNPs (namesand chromosome coordinates) with QASs and p-values for each testdescribed in Table 3.

2.) MANHATTAN plots are computed as well to compare all genotyped SNPsfor any single test (FIG. 3).

3.) The HIGHWAY feature is a dynamic human chromosome browser thatscrolls in a 3-dimensional moving video mode above a “chromosomalsurface” whereby the baseline transversal axis is a list of tests (e.g.,123 ARG association tests for Group A in Table 3) and the longitudinalaxis lists the SNPs ordered along each of the 22 human autosomes.Embodiments of the longitudinal axis units can be distance along thechromosome in number of SNPs, nucleotide base pairs, centimorgans or inrecombinant frequency.

Blocks rising out of the surface reflect −log p-values (for p<10⁻⁴, 10⁻³or 10⁻² depending on the user-controlled settings, to remove noise ofnon-significant results), while the color intensity reflects the QAS.Certain colors, such as red, of the bar may indicate disease susceptible(QAS>1.0) SNP-allele association outcomes, and other colors, such asgreen, may indicate SNP-allele association suggesting resistance todisease (QAS<1.0). The browser allows the user to navigate above thechromosome surface and view more detailed association statistics(p-value, QAS) as well as SNP information (rs-number, coordinates, MAFetc.) by placing the mouse pointer atop the bar for any SNP-testcombination. A user can search and locate any single region of interestby SNP rs-number, gene name, chromosome coordinates or the threshold for−log p-value. This search can be performed within the currently selectedchromosome or whole genome-wise. Therefore, a region identified as apotential hit in any other study, whether it includes the same SNPs ornot, can be targeted and inspected for neighboring or relatedassociations in the region.

For any region of interest the user may be able to access additionalinformation and insight as follows:

4.) GWATCH can produce a 2-Dimensional Snapshot (2D-SNAPSHOT), anordered list of SNPs (e.g., ˜80 SNPs at 4 kb average density) and testresults (e.g. 123 tests for Group A, Table 2) equaling ˜10,000 SNP-testcombinations. The 2D-SNAPSHOT of a selected genomic region may be a heatplot indexed by the p-values from p>0.05 (such as light grey) to deepricher colors for decreasing p-values, assuring that significant regionclusters are more densely colorful (FIGS. 5A-5L). The human brain,trained to understand genetic analysis can grasp the visuals provided incertain embodiments herein almost instantly.

5.) GWATCH may also display 3-Dimensional Snapshot (3D-SNAPSHOT) of aselected region similar to the view in the dynamic HIGHWAY, but labeledwith SNP rs-number, chromosome coordinates and MAF. FIGS. 6A-6P shows a3D-SNAPSHOT of the region that includes described AIDS resistance genePROX1.

6.) Since each SNP allele that is used to calculate individualQASs/p-values along the HIGHWAY browser is by default the minor allelein the study population, in some cases the colors (red and green) may bedeceptive.

This is because an association of multiple linked SNPs may include LDbetween a minor (less common) allele at one locus and a common allele atan adjacent polymorphic site. Therefore, one minor allele may signal assusceptible (red) while the nearby minor allele may signal as resistant(green) although they are both tracking the same association effect. Toassuage this computational artifact, GWATCH may include a “Polarize”option that, for any selected region, computes the direction of LD (+ or−LD) between the minor allele at the currently selected SNP (indexedSNP) and all minor alleles at adjacent SNPs. When there is a“discordant” LD relationship (i.e., the minor allele of the index locustracks a common allele of the adjacent SNP variant), GWATCH may computethe multiplicative inverse (1/QAS) and color the HIGHWAY view and3D-SNAPSHOT according to these inverted “polarized” QAS values. When theentire association signal for a region, driving the non-independent SNPsand non-independent tests, derives from a single causal allele withinthe region, the blocks of associated SNPs in the viewed region should bethe same color after polarization (FIGS. 7A-7P). This exercise may helpincrease confidence that the association signal represents a true signaldriven by a causal variant tracked by proxy SNPs and not a statisticalor genotyping artifact. 2D- and 3D-SNAPSHOTs and POLARIZED 3D-SNAPSHOTsare presented from previously validated ARG regions (PARD3B and PROX1)in FIGS. 5A-5L, FIGS. 6A-6P, and FIGS. 7A-7P.

7.) The TRAX option may provide a more detailed report of testsperformed for a single SNP. The p-values and QASs may be presented as aTRAX PAGE (FIGS. 8A-8B) which summarizes all test results for a singleSNP (e.g., all the p-values and QASs for HIV infection, AIDS progressionusing categorical and survival analyses, AIDS sequelae, and HAARToutcomes can be viewed in one track).

8.) GWATCH may produce in real time a detailed TRAX REPORT of derivedstatistics for all the tests accomplished including tables, bar graphs,survival curves and additional parameters for each test (FIGS. 9A-9B).TRAX REPORTs may be accessed by selecting the block of interest inHIGHWAY and clicking the “TRAX REPORT” option. GWATCH may compute andgenerate the report in real time (e.g., for 641 SNPs in 241 genes fromexemplary dataset that were genotyped by PCR as replication ofAffymetrix GWAS genotypes). The TRAX REPORT employs the underlyingconfidential genotype and clinical data but does not actually reveal anyof this information publicly, thereby assuring protection of patientconfidentiality. The primary confidential information on genotypes andclinical data may be securely stored on a server and not be accessibledirectly.

For any or all stages of disease (e.g., infection, progression,sequelae, or treatment; Table 3) GWATCH may compute a list of SNP-testcombinations ranked according to p-values or QASs for the first pass atidentifying association HITS thereby providing an association discoverytool. Further, GWATCH may identify chromosomal regions that showremarkably high density of significant p-values across closely linkedSNPs and related tests for a disease stage (See below in “Statisticaltools for the whole genome analysis”).

GWATCH may enable investigators and users not connected to the originalstudy to access the results of SNP association (from the whole genomesequencing or SNP array genotyping) to view and share their study designand results openly. It can be used for visualization of regions withsignificant p-values to inspect the pattern of variation across linkedSNPs and also at different stages of disease (e.g., HIV infection, AIDSprogression, AIDS sequelae and treatment outcome).

As a primary discovery approach, screening across unabridged testresults poses large statistical penalties for multiple tests erodingconfidence in associations that fail to achieve genome-widesignificance. For this reason, one should use caution in inspection ofputative regions of significance. Nonetheless, wholesale discarding ofmarginally significant “hits” may discount some true associations withinthe mix of statistical artifacts. GWATCH may offer an opportunity toscreen the genome for disease-associated regions, which may containcausal SNP variants included (or not) in the SNP array used forgenotyping, as well as proxy SNPs tracking the causal variant. Further,in complex diseases for which there are many different cohorts beingstudied (in HIV-AIDS there are at least twenty different groupsconducting AIDS GWAS on small, well-defined cohorts that may differ ingenetic background and clinical data available for association testing)GWATCH may offer rapid replication opportunities with an independentdataset.

To demonstrate GWATCH, three previously validated AIDS resistance generegions, CCR5-Δ32, PROX1 and PARD3B, can be examined by simply enteringrs-number, gene name or chromosome coordinates in the search option (see2D image of PARD3B region in FIGS. 5A-5L and 3D images of PROX1 regionin FIGS. 6A-6P and FIGS. 7A-7P). GWATCH moves HIGHWAY browser to theselected region so one can visualize the signal with the 2D and3D-SNAPSHOTS plus the TRAX REPORTS that show the results clearly.

The GWATCH web browser may provide a dynamic visual journey, similar todriving in a video game along human chromosomes to view patterns ofGWAS- or whole genome sequencing (WGS)-based variant association withany complex disease. It is meant to be appealing, intuitive, andaccessible to non-experts and experts alike, including the variouscontributors to today's exciting gene association studies. The formatand open web access may allow for importing new data from anydisease-gene association study with multiple disease stages or geneticmodels of analysis. The wide breadth of test associations displayed isparticularly suited to complex disease cohorts with detailed clinicalparameters over distinct disease stages. Further, although GWATCH ispotentially useful for initial gene discovery, an important corollarylies in providing rapid replication of gene discoveries from independentcohort studies by simply keying in the putative gene region andinspecting the many test results of the posted dataset. Sincereplication screens are hypothesis-driven, they avoid the stringentmultiple test correction penalties of a GWAS/WGS (p<10⁻⁸). Finally,different cohort studies can be compared directly or combined to buildmeta-analyses.

GWATCH is a generalizable web tool suitable for GWAS and/or WGS datasetfor any complex disease. The “finished” or “processed” data (onescontaining a final Data Table of p-values and QAS for completedassociation tests) can be uploaded directly. “Primary” or “unfinished”data (ones with genotypes and clinical data for which tests need to beconstructed and calculated) may be uploaded for custom development of adisease-specific GWATCH-based analysis.

Should many cohort investigators release their unabridged results, thenassociation discoveries may be replicated (or not) in a rapid, open andproductive manner, allowing for large meta-analyses as have beenproposed for HIV-AIDS and other complex diseases. Unlike other methodsof data sharing, this results-based open data sharing/release approachmay avoid any violation of patient privacy, IRB and HIPAA concerns, orinformed consent constraints, since the primary clinical and genotypedata remain confidential while the derivative results (p-values, QASs,plots) of multiple conceivable analytical approaches are openlyreleased. In this approach, discovery and replication opportunities inimportant biomedical research may be expanded. This may ensure themaximum benefit of open access data sharing while protecting patientswho prefer privacy but wish to see their volunteerism fulfilled.

Exemplary Implementation

GWATCH may be a web-based application that integrates severaltechnologies and programming languages. Other types of applications suchas server-based, hardware-based, etc. may be used. In web-basedapplications, the server-side may be represented by an Apache webserver, which employs, for example, a PHP engine and a Java-basedtoolkit BATIK. R-PROJECT functions and modules may be used forperforming statistical tests, polarization and density calculation. Adatabase component of GWATCH may be implemented using MySQL and mayallow access, retrieval and management of genotypes, clinicalinformation and test results. On the front end, GWATCH may employ HTML5,Javascript, jQuery and WebGL for HIGHWAY browser interface, and Ajax andJSON technologies for data exchange between server and client.

Exemplary Statistical Methods

Different tests for analysis of associations in the various types ofdata are included in GWATCH. A user having clinical and genotype datafor one or several populations can select an appropriate group of testsfor screening statistical associations of infection or diseaseprogression with genotypes. The results of all selected statisticaltests may be visualized simultaneously in the HIGHWAY browser. Most ofthe statistical tests may be executed using R-project. In addition, aspecial option may enable display of detailed results of statisticalanalysis for any selected SNP via a TRAX REPORT.

Statistical Data

Input data for the analysis may consist of both clinical data andgenotype data. The genotype data may contain information on all SNPs tobe analyzed, and the corresponding genotype consists of two dummy(binary) variables specifying corresponding forms of two alleles or ofone categorical variable with three levels specifying whole genotypeinformation for the individual. In the former case, 0 may correspond tocommon SNP allele and 1 may correspond to minor SNP allele. In thelatter case, common SNP homozygote may be coded by −1, minor SNPhomozygote may be coded by 1 and heterozygote may be coded by 0. Therequired SNP information may be SNP identifier (SNP ID) and thecorresponding coordinate. The input genotype data are expected to besorted by SNP ID and/or by SNP coordinate sequentially. For furtheranalysis, all individuals may be subjected to different types ofgenotype classification.

Genotype classification may be used as an explanatory factor for allstatistical tests. Four types of genotype classification may be used:dominant (D) classification separates common homozygote from all othergenotypes in two different groups; recessive (R) classificationseparates minor homozygote from all other genotypes; and co-dominant(CD) classification separates all individuals into three groups by theirgenotype; under allelic classification (A), two SNP allelescorresponding to any single individual are considered as differentobservations with the same clinical data.

In certain embodiment, and for certain diseases, various types ofclinical data may be acceptable for analysis, including, but not limitedto: categorical and right-censored survival.

Categorical data may include the ID variable and numeric categoricalvariable having two or more levels specifying disease status. In thecase of two levels, it is recommended to use code 1 for affectedindividuals (i.e., individuals which acquire infection, demonstratesymptoms of disease etc.) and code 0 for other individuals. In the caseof more than two levels and ordinal categories it is recommended to use0 for unaffected individuals (e.g., uninfected individuals orindividuals with no disease symptoms etc.) and to choose positivenumbers corresponding to other levels in the same order as the originalcategories.

Right-censored survival data may contain information on the exact timefrom baseline date (preferentially in days) to an event and type of theevent that is given by the binary variable: 1 corresponding to failure(event occurred) and 0 corresponding to censoring (no event), for anyindividual. For the competing risks model it is possible to use severalpositive levels for different types of failures.

Statistical Tools for Associations

GWATCH software may allow analysis of disease associations with genotypefor all available SNPs. Tests corresponding to different genotypeclassifications can be produced for any clinical data by the selectedtesting method. Stratified analysis may be available if the inputclinical data contain a classification variable. In this case, anyselected group of individuals may be analyzed separately and the resultsof these tests are displayed on different lanes of the Highway.

Categorical tests (CT) may be used for categorical statistical analysisof data organized as an mxk contingency table. The categorical data arerequired to perform categorical tests. Fisher's exact test (R-function“fisher.test( )”) for 2×2 contingency tables and chi-square test(R-function “chisq.test( )”) may be applied to produce p-value. The oddsratio for 2×2 contingency tables or the (1−ρ)/(1+ρ) transformation ofPearson's correlation coefficient (designated as ez2-transformation forthe square of the exponentiated Fisher's z-transformation) for thetables of other sizes define direction of the association, andtherefore, color of the corresponding bar on the Highway.

Proportional hazards survival tests (PHST) may be used for the analysisof right-censored survival data. The Cox proportional hazards model maybe used to produce p-value (R-function coxph( ), package survival). Thedirection of the associations is defined by the obtained relativehazard, which is calculated as hazard ratio (for binary genotypeclassifications A, D and R) or exponentiated slope of Cox's regressionline (under CD genotype classification).

Categorical tests for survival data (CTSD) may be used to identifysignificant differences between categories of individuals grouped byfailure times. The right-censored survival data are required to performcategorical survival tests. The baseline null hypothesis may beformulated in terms of the identity of cumulative distribution functionscorresponding to different groups of individuals. Individuals involvedin the analysis may be classified by observed failure or censoring timesaccording to specified rules.

Hardy-Weinberg equilibrium (HWE) tests may be performed to evaluatesignificant deviation from Hardy-Weinberg equilibrium that is commonlyused as an indicator of genotyping errors. Haldane's exact test onHardy-Weinberg equilibrium may be used to produce p-values. Sign ofHardy-Weinberg disequilibrium statistic may be applied to specifydirection of the disequilibrium. The R-function HWExact( ) ofHardyWeingerg package may be used to perform HWE test.

For the convenience of test results representation, several differentstatistics, which describe the direction and strength of associationbetween a SNP and disease characteristic in different tests (odds ratio,relative hazard and ez2-transformed correlation coefficient), may becombined under the general term of Quantitative Association Statistic(QAS). The QAS takes positive values. Values of QAS>1 and QAS<1correspond to positive and negative associations, respectively.

Trax Reports

A TRAX REPORT tool may provide for predetermined or variable display ofinformation obtained from the systems and methods described herein.After screening for associations of clinical traits and genotypes onemay be interested in closer review of certain SNPs. The TRAX REPORT toolmay allow production of reports on extended statistical analysis for anysingle SNP if the corresponding genotype information available for allindividuals. Important genotype information may be given in the headeron the TRAX front page: SNP identifier, SNP coordinate, chromosome,alleles and their frequencies. A header may also list information onpopulations involved into analysis. In addition to the header, a frontpage may also contain summary for all tests with p-values and values ofQAS represented for all tests in the bar plot form. Following pages ofthe TRAX REPORT may contain detailed information: contingency tables areproduced in the form of corresponding bar plots for any categorical test(including progression categorical tests) and Kaplan-Meier survivalcurves are reported for all three genotypes for all survival tests.

FIGS. 8A-8B shows an embodiment of a TRAX PAGE, which is a summary ofall test results for a single SNP for a study group (e.g., p-values andQASs for HIV infection, AIDS progression using categorical and survivaltests, AIDS sequelae, and HAART outcomes can be viewed and compared).TRAX PAGE can be generated de novo for any SNP of interest by placingmouse tip over a significant bar in the HIGHWAY browser and selectingthe TRAX PAGE option from the data window that appears.

FIGS. 9A-9B shows an embodiment of a section of a detailed multi-pageTRAX REPORT illustrating a section of line graphs. A TRAX REPORT mayadditionally be comprised of derived statistics for all the testsaccomplished including tables, bar graphs, survival curves andadditional parameters for each test, and the like. A TRAX PAGE can begenerated de novo for any SNP of interest by placing mouse tip over asignificant bar in HIGHWAY browser and selecting the TRAX PAGE optionfrom the data window that appears.

Statistical Tools for the Whole Genome Analysis

Several statistical tools addressing SNP compositions may be available.

Polarization tool may allow adjustment of QAS results for minor andcommon SNP-alleles around some fixed SNP (called index SNP) for betterapproximations of true associations. A polarization table may beproduced using linkage disequilibrium coefficients (D′) betweenneighboring SNPs. Linkage disequilibrium coefficients may be calculatedfor 80 SNPs upstream and 80 SNPs downstream of the index SNP. This cansimilarly be calculated for 20, 40, 60, 100 or 120 or more upstream ordownstream SNPs, with the choice generally depending on the density ofthe SNPs in the general location of interest. Other numbers may be useddepending on particular applications. In case of sufficiently largepositive value of linkage disequilibrium (D′>0.9), the polarization markmay be assigned to 1, whereas in case of sufficiently large negativelinkage disequilibrium (D′<−0.9) the polarization mark may be assignedto −1. If linkage disequilibrium is sufficiently small, the polarizationmark may be assigned to 0. In the process of polarization, QAS valuesfor test results of neighboring SNPs may be inverted if the polarizationmark is −1 implying the inversion of direction of disease associationfor such SNPs.

Manhattan plots of −log p-values may be produced for any single test forall available SNPs. A Manhattan plot may be a type of scatter plot,usually used to display data with a large number of data-points—many ofnon-zero amplitude—and with a distribution of higher-magnitude values.FIG. 3 shows an example of an embodiment of a Manhattan plot.

Significant regions (regions of concentration of small p-values fordensity top scoring) may be identified using density feature. Densitytop scoring that identifies regions of concentration of small p-valuesmay be calculated for each SNP in two steps:

1) in the window of specified size (n SNPs upstream and downstream or nKbp upstream and downstream) average −log p-value may be computed foreach test (lane of the Highway)

2) these per-test (per-lane) averages are used for calculating densityat this SNP either by averaging them or by finding the largest one(depending on the option chosen)

The second step can be performed for all the tests or for the group oftests by the disease stage (e.g., all tests for HIV infection, all testsfor AIDS progression etc.).

Statistical Tests and Data Used for Complex AIDS Study

To illustrate GWATCH utility in the analysis of GWAS results, data frommulticenter longitudinal studies of several cohorts of patients exposedto the risk of HIV infection and/or already infected with HIV were used:ALIVE, DCG, HGDS, HOMER, LSOCA, MACS, MHCS and SFCC. The total pool ofpatients was divided into three groups A, B and C based on ethnicity andtiming of data development (see Table 2). A total of 5,922 patients wereanalyzed in all 3 groups.

All patient samples and genotypes may be subjected to quality controlfiltering. Once final genotypes may be obtained, population structurecan be assessed using the Principal Components Analysis module ofEigensoft software in European and African American populations andstructured SNP variants may be excluded.

Following statistical tests may be applied to the analysis of HIV-AIDSresearch data obtained from these patients. For each of the testsdescribed below 3 genetic models may be used (D, R and CD, see aboveunder “Genotype classification”).

Infection Tests (INF).

Infection tests may be used to specify the association of any selectedgenotype with HIV infection. The original clinical data may be ofcategorical type based on the population of seronegatives (SN,individuals which stay HIV-negative throughout the whole study) at thebaseline with the response variable indicating serostatus at theendpoint and having three levels: “high risk exposed uninfected” (HREU)seronegatives, “other seronegatives” (OSN) and “seroconverters” (SC,individuals which entered the study as HIV-negative, but becameHIV-positive during the study). Three combinations of HIV statusclassifications can be used to perform the categorical tests: “SC” vs.“HREU”, “SC” vs. “HREU” plus “OSN” and “SC” vs. “HREU” vs. “OSN”. Inaddition to the three genotype classifications described above (D, R andCD), allelic model (A) was also used for this test. One more group ofindividuals based on infection status, “seroprevalents” (SP, individualswhich entered the study already being HIV-positive), was not informativefor this type of test and therefore was not included in it.

Disease Progression Tests.

The disease progression tests can be used for screening significantassociations between AIDS progression and genotype. The original datawere of right-censored survival type under four different criteria ofAIDS disease: CD4<200 (level of CD4+ cells falling below 200 cells/mm³),AIDS-1987 (patient meeting criteria of 1987 CDC definition of AIDS),AIDS-1993 (patient meeting criteria of 1993 CDC definition of AIDS) andDeath from AIDS. Only seroconverter and seroprevalent individuals wereincluded in this analysis. Seroconverter individuals may be includedinto analysis with HIV infection date (date of seroconvertion) as thebaseline. Seroprevalent individuals may be included into categoricalanalysis with the date of the first visit as the baseline with somewarnings.

Disease progression categorical analysis (PDCA) may use the categoricaltests for survival data (CTSD) approach described above. The CTSD can beperformed in dichotomous (PDCA2, two groups by the survival time orcurrent status data) and multipoint (PDCAM, more than two groups by thesurvival time) forms. All individuals censored before the breakpoint maybe removed from the PDCA dichotomous analysis, as well as theseroprevalent individuals who failed before the breakpoint. Allremaining individuals censored or failed after the breakpoint may beclassified into the group of long-term survivors (LTS, those who do notshow AIDS symptoms before the breakpoint).

Proportional hazard (PHAZ) analysis of disease progression may use theproportional hazards survival tests (PHST) approach described above.These tests may be performed for all four criteria of AIDS. Onlyseroconverter individuals were included into PHAZ analysis.

Sequelae Tests.

Survival and categorical tests may be performed for survival data onKaposi's sarcoma (KS), Pneumocystis carnii pneumonia (PCP),cytomegalovirus infection (CM), lymphoma (LY), mycobacterial infection(MYC) and other opportunistic infections (OOI). As in progressiondisease tests, survival sequelae tests may include seroconverters only,while categorical sequelae tests may include both seroconverters andseroprevalents.

Sequelae tests for any infection order may classify patients based onwhether specific sequela occurred at all, irrespectively of its order(i.e., whether it was the first sequela to occur for patient). Thesurvival tests (SEQSA) under proportional hazards model as well as theprogression categorical tests (SEQCA) may be performed separately foreach of the diseases described above.

Sequelae tests for the first infection may classify patients based onwhether specific sequela occurred first or not. The survival tests(SEQS1) under proportional hazards model as well as the progressioncategorical tests (SEQC1) may be performed separately for each of thediseases described above.

Highly Active Antiretroviral Therapy (HAART).

HAART tests may be performed for the cohorts of patients who weresubject to this type of treatment. Patients may be classified based oneither the level of suppression of HIV viral load or on the rebound ofviral load following its suppression. Both survival (HRTS) andprogression categorical (HRTC) tests may be used for this analysis.

Hardy-Weinberg Equilibrium Tests (HWE).

The HWE tests may be performed to control for the quality of data usedfor the screening of associations. Large deviations from Hardy-Weinbergequilibrium are not typical for the large populations and thus signalthe genotyping error or some other type of data quality breach.

TABLE 1 Potential Features of GWATCH. Representative Features displayedFigure 1. Unabridged Data Table of SNP chromosome — coordinates, MAF*,p-value and QAS** for each SNP for each test 2. Association Tests Listand Manhattan Plots for FIG. 3 each test across all SNPs 3. SNAPSHOTS ofSNP-test results in a chromosome region:   1. 2D Heat Plot Snapshotillustrating p-values in any FIG. 5   selected chromosome region   2. 3DCheckerboard Plot Snapshot illustrating p- FIG. 6   values and QAS** inany selected   chromosome region   3. LD-polarized 3D CheckerboardSnapshot FIG. 7   illustrating p-values and QAS** in any selected  chromosome region 4. Dynamic Highway View by Chromosome Browser FIG. 4illustrating p-values and QAS** 5. Top association hits:   1. Top hitsbased on ranked −log p-value —   2. Top hits based on ranked QAS** —  3. Top hits based on ranked Density of −log p-value —   within a SNPgenomic region 6. TRAX feature:   1. TRAX PAGE - two-page graphicsummary FIG. 8   illustrating p-values and QAS** for one selected   SNP  2. TRAX REPORT - eleven-page analysis summary FIG. 9   with graphs,curves and tables for all association   tests for one selected SNPAbbreviations: *MAF—minor allele frequency; **QAS—quantitativeassociation statistic (OR, RH, ez2-transformed correlation coefficient).

TABLE 2 Patient categories and counts in Groups A-C. Number of patientsfor each group Group A Group B Group C Total Abbreviation Risk groupsEA*-I EA*-Total AA** B + C HREU High Risk Exposed HIV 254 300 148   448Uninfected EU (except HREU) Exposed HIV Uninfected (all  1 351 267   618risks) SC Sero-Convertor 703 767 288 1 055 SP-LTSSero-Prevalent-Long-Term- 444 831 170 1 001 Survivor (no AIDS for >10years) Sequelae AIDS sequelae diagnosis 461 1 848    0 1 848 HAARTAnti-retroviral treatment 485 1 319    65 1 384 Total study 1 527   4462   1 460   5 922 participants Abbreviations: *EA—European Americans;**AA—African Americans.

TABLE 3 Tests per study group. Number of tests for each group Clinicalstage Test type Group A Group B Group C I. HIV Infection Ia. Infection -categorical 3 12 12 II. HIV Progression IIa. Progression - categorical12 12 12 dichotomous IIb. Progression - categorical multipoint 12 12 12IIc. Progression - survival 48 48 24 III. AIDS defining IIIa. Sequelae -categorical first sequela 9  9 — Conditions IIIb. Sequelae - survivalfirst sequela 9 — — IIIc. Sequelae - categorical any sequelae 9 33 —IIId. Sequelae - survival any sequelae 9  6 — IV. Treatment with ARVIVa. HAART - categorical 6 — — IVb. HAART - survival 6 12 — Total 123144  60

Although not required, the systems and methods are described in thegeneral context of computer program instructions executed by one or morecomputing devices that can take the form of a traditionalserver/desktop/laptop; mobile device such as a smartphone or tablet;etc. Computing devices typically include one or more processors coupledto data storage for computer program modules and data. Key technologiesinclude, but are not limited to, the multi-industry standards ofMicrosoft and Linux/Unix based Operating Systems; databases such as SQLServer, Oracle, NOSQL, and DB2; Business Analytic/Intelligence toolssuch as SPSS, Cognos, SAS, etc.; development tools such as Java, .NETFramework (VB.NET, ASP.NET, AJAX.NET, etc.); and other e-Commerceproducts, computer languages, and development tools. Such programmodules generally include computer program instructions such asroutines, programs, objects, components, etc., for execution by the oneor more processors to perform particular tasks, utilize data, datastructures, and/or implement particular abstract data types. While thesystems, methods, and apparatus are described in the foregoing context,acts and operations described hereinafter may also be implemented inhardware.

FIG. 1 shows an exemplary system 100 for visualization, sharing andanalysis of large data sets according to one embodiment. In thisexemplary implementation, system 100 may include one or moreservers/computing devices 102 (e.g., server 1, server 2, . . . , servern) operatively coupled over network to one or more client computingdevices 106-1 to 106-n, which may include one or more consumer computingdevices, one or more provider computing devices, one or more remoteaccess devices, etc. The one or more servers/computing devices 102 mayalso be operatively connected, such as over a network, to analyticalsoftware 104 and/or one or more databases 108 (e.g., database 1,database 2, . . . , database n). The one or more databases 108 mayinclude data storing and/or data retrieval capabilities. The analyticalsoftware 104 may provide for data processing, such as format conversionand/or statistical analysis. Various devices may be connected to thesystem, including, but not limited to, client computing devices,consumer computing devices, provider computing devices, remote accessdevices, etc.

Server/computing device 102 may represent, for example, any one or moreof a server, a general-purpose computing device such as a server, apersonal computer (PC), a laptop, a smart phone, a tablet, and/or so on.The server/computing device 102 may provide one or more interfaces fordata input and output. The server/computing device 102 may provide a 3Ddynamic graphical engine. Networks may represent, for example, anycombination of the Internet, local area network(s) such as an intranet,wide area network(s), cellular networks, WIFI networks, and/or so on.Such networking environments are commonplace in offices, enterprise-widecomputer networks, etc. Client computing devices 106, which may includeat least one processor, may represent a set of arbitrary computingdevices executing application(s) that respectively send data inputs toserver/computing device 102 and/or receive data outputs fromserver/computing device 102. Such computing devices include, forexample, one or more of desktop computers, laptops, mobile computingdevices (e.g., tablets, smart phones, human wearable device), servercomputers, and/or so on. In this implementation, the input datacomprises, for example, genome data, clinical data, phenotypic dataand/or so on, for processing with server/computing device 102. In oneimplementation, the data outputs include, for example, emails,templates, forms, and/or so on. Embodiments of the present invention mayalso be used for collaborative projects with multiple users logging inand performing various operations on a data project from variouslocations. Embodiments of the present invention may be web-based, smartphone-based and/or tablet-based or human wearable device-based. Each ofthe client computing devices 106 may upload research data, including rawor calculated data. One or more of the client computing devices 106 mayview data through an interface and/or download data, tables, top hits,etc.

In this exemplary implementation, server/computing device 102 includesat least one processor coupled to a system memory. System memory mayinclude computer program modules and program data.

In this exemplary implementation, server/computing device 102 includesat least one processor 202 coupled to a system memory 204, as shown inFIG. 2. System memory 204 may include computer program modules 206 andprogram data 208. In this implementation program modules 206 may includedata module 210, analysis module 212, output module 214, and otherprogram modules 216 such as an operating system, device drivers, etc.Each program module 210 through 216 may include a respective set ofcomputer-program instructions executable by processor(s) 202. This isone example of a set of program modules and other numbers andarrangements of program modules are contemplated as a function of theparticular arbitrary design and/or architecture of server/computingdevice 102 and/or system 100 (FIG. 1). Additionally, although shown on asingle server/computing device 102, the operations associated withrespective computer-program instructions in the program modules 206could be distributed across multiple computing devices. Program data 208may include genome data 220, patient data 222, study data 224, and otherprogram data 226 such as data input(s), third party data, and/or others.Although it is contemplated that the computerized application of theembodiments described herein may be incorporated on a number of types ofcomputing devices, the systems and methods necessarily require the useof some type of computing device. This is because the embodiments of thesystems and methods described herein may be utilized to analyze and/ordisplay large amounts of data that could not possibly be accomplishedwithout the aid of some type of computing device.

In some embodiments, an exemplary system may include specialized geneticequipment. Specialized genetic equipment may include genetic sequencers,various polymerase chain reaction equipment and associated methods, andthe like.

Embodiments described herein may permit individuals to scan very largedata sets with a lower loss of attention to detail than presently usedto review large data sets. This may permit those analyzing large amountsof data to more accurately visualize trends and minor differences. Whilenot wishing to be constrained by a present theory, it is believed thatthis greater accuracy arises from a human ability to gauge threedimensional information more rapidly than the same information displayedin a two dimensional manner or tabular, non-graphical manner.

Although the foregoing description is directed to the preferredembodiments of the invention, it is noted that other variations andmodifications will be apparent to those skilled in the art, and may bemade without departing from the spirit or scope of the invention.Moreover, features described in connection with one embodiment of theinvention may be used in conjunction with other embodiments, even if notexplicitly stated above.

What is claimed is:
 1. An automated computerized method forvisualization, replication, sharing, or analysis of large data sets, thecomputerized method comprising the steps of: receiving an input dataset, wherein the input data set comprises data that can be classified inclassification dimensions wherein a first classification dimension is alinear ordering of data entries and a second classification dimension;obtaining an unabridged data table listing results for each combinationof coordinates in the first classification dimension and the secondclassification dimension; and displaying contents of the unabridged datatable as a visual array wherein two axes correspond to the coordinatesand a third axis corresponds to a third dimension, wherein the thirddimension represents an actual value of the respective data point forthe coordinates, and expanding the display of a result of a genome-wideassociation study (GWAS) data set for genome-wide variants includingreal-time replication of candidate or putative genes to limitstatistical penalties in the visual array, wherein the visual array isdepicted as a three-dimensional image that can be displayed as a movingvideo mode above a surface wherein two axis in the horizontal planerepresent the first classification dimension and the secondclassification dimension, while height of blocks rising from that planerepresent the third dimension.
 2. The method of claim 1, wherein thevisual array comprises a fourth dimension that represents an additionalcomponent of the data point value.
 3. The method of claim 2, wherein thefourth dimension is represented by color or shape.
 4. The method ofclaim 1, wherein the input data set is classified in two dimensions, andfurther comprising reformatting the input data set to create theunabridged data table.
 5. An automated computerized method forvisualization, replication, sharing, or analysis of large data sets, thecomputerized method comprising the steps of: analyzing an input data setor converting the input data set to obtain an unabridged data table;receiving the input data set, wherein the input data set comprises datathat can be classified in classification dimensions wherein a firstclassification dimension is a linear ordering of data entries and asecond classification dimension; receiving the unabridged data tablelisting results for each combination of coordinates in the firstclassification dimension and the second classification dimension;displaying contents of the unabridged data table as a visual arraywherein two axes correspond to the coordinates and a third axiscorresponds to a third dimension, wherein the third dimension representsan actual value of the respective data point for the coordinates; andexpanding the display of a result of a genome-wide association study(GWAS) data set for genome-wide variants including real-time replicationof candidate or putative genes to limit statistical penalties in thevisual array.
 6. The method of claim 5, wherein the analysis comprisesassessment of the shape of a three-dimensional surface landscape in thevisual array.
 7. The method of claim 6, wherein assessment comprisesidentification of regions of high or low density of statistical signals.8. An automated computerized method for visualization, replication,sharing, or analysis of large data sets, the computerized methodcomprising the steps of: receiving an input data set, wherein the inputdata set comprises data that can be classified in classificationdimensions wherein a first classification dimension is a linear orderingof data entries and a second classification dimension, and wherein theinput data set is the result of a genome-wide association studies (GWAS)or whole genome sequencing (WGS) association study or its analysis,obtaining an unabridged data table listing results for each combinationof coordinates in the first classification dimension and the secondclassification dimension; displaying contents of the unabridged datatable as a visual array wherein two axes correspond to the coordinatesand a third axis corresponds to a third dimension, wherein the thirddimension represents an actual value of the respective data point forthe coordinates, and expanding the display of a result of a genome-wideassociation study (GWAS) data set for genome-wide variants includingreal-time replication of candidate or putative genes to limitstatistical penalties in the visual array.
 9. The method of claim 8,wherein a first axis of the visual array represents single nucleotidepolymorphisms (SNPs) linearly ordered according to their genomicpositions, a second axis represents different association testsperformed, height of blocks rising above the surface represents reversedsign decimal logarithm of p-value, and color represents quantitativeassociation statistics.
 10. The method of claim 9, wherein thequantitative association statistics represent direction and strength ofassociations for association tests and may be selected from the groupconsisting of, odds ratio, relative hazard, Pearson's correlationcoefficient (ez2-transformed), and combinations thereof.
 11. The methodof claim 10, wherein an output contains one or more of the followingfeatures: data table listing all SNPs, comprising names and chromosomecoordinates, with p-values and quantitative association statistics(QASs) for each test; Manhattan plot showing p-values for all genotypedSNPs for any single test, computed for all tests; three-dimensionalmoving video mode allowing navigation above a chromosome surface andviewing more detailed association statistics and SNP information;two-dimensional snapshot of a selected genomic region in the form of aheat plot indexed by the p-values; three-dimensional snapshot of aselected genomic region in the form of a static image of athree-dimensional moving video mode; polarized three-dimensionalsnapshot of a selected genomic region where values of QAS are invertedaccording to the value of linkage disequilibrium (LD) between minorallele of the index SNP and those of other neighboring SNPs; summary ofall test results for a single SNP; analytical report of all testsperformed for a single SNP; and list of SNP-test combinations rankedaccording to p-values, QASs or density of significant p-values acrossclosely linked SNPs and related tests for a disease stage.
 12. Themethod of claim 11, further comprising receiving a search queryregarding the visual array to locate any single region of interest bySNP rs-number, gene name, chromosome coordinates or the threshold for−log p-value within the currently selected chromosome or throughout thewhole genome and displaying this region of interest.
 13. The method ofclaim 12, wherein the analysis utilizes genomic data in the form ofpatients' genotypes and clinical (phenotypic) data selected from thegroup consisting of: categorical clinical data, right-censored survivalclinical data, and combinations thereof.
 14. The method of claim 13,wherein the analysis comprises tests selected from the group consistingof: categorical tests, proportional hazards survival tests, categoricaltests for survival data, Hardy-Weinberg equilibrium tests, andcombinations thereof.
 15. The method of claim 1, wherein the input dataset comprises results of analysis of an initial data set.
 16. The methodof claim 1, further comprising performing meta-analyses of the visualarray in a dynamic browser displaying association results.
 17. Anautomated computerized method for visualization, replication, sharing,or analysis of large data sets, the computerized method comprising thesteps of: anonymization of a genomic data set to produce an input dataset; receiving the input data set, wherein the input data set comprisesdata that can be classified in classification dimensions wherein a firstclassification dimension is a linear ordering of data entries and asecond classification dimension; obtaining an unabridged data tablelisting results for each combination of coordinates in the firstclassification dimension and the second classification dimension;displaying contents of the unabridged data table as a visual arraywherein two axes correspond to the coordinates and a third axiscorresponds to a third dimension, wherein the third dimension representsan actual value of the respective data point for the coordinates, andexpanding the display of a result of a genome-wide association study(GWAS) data set for genome-wide variants including real-time replicationof candidate or putative genes to limit statistical penalties in thevisual array.
 18. The method of claim 17, further comprising isolatingand sampling Deoxyribonucleic acid (DNA) to produce the genomic dataset.
 19. An automated computerized method for visualization,replication, sharing, or analysis of large data sets, the computerizedmethod comprising the steps of: receiving an input data set, wherein theinput data set comprises data that can be classified in classificationdimensions wherein a first classification dimension is a linear orderingof data entries and a second classification dimension, obtaining anunabridged data table listing results for each combination ofcoordinates in the first classification dimension and the secondclassification dimension; and displaying contents of the unabridged datatable as a visual array wherein two axes correspond to the coordinatesand a third axis corresponds to a third dimension, wherein the thirddimension represents an actual value of the respective data point forthe coordinates, and wherein a large data set is a genome-wideassociation studies (GWAS) or a whole genome sequence (WGS) analysis,and expanding the display of a result of a genome-wide association study(GWAS) data set for genome-wide variants including real-time replicationof candidate or putative genes to limit statistical penalties in thevisual array.
 20. A computerized method of displaying large data sets,the method comprising: receiving an input data set, wherein the inputdata set comprises data that can be classified in two classificationdimensions; displaying the input data set in a graph that have three ormore output dimensions; and allowing a user to navigate above a plane oftwo output dimensions from the graph, and wherein the plane is arepresentation of a chromosome, wherein the user views associationstatistics and single nucleotide polymorphism (SNP) information,displaying contents of the large data set as a visual array wherein twoaxes correspond to the coordinates and a third axis corresponds to athird dimension, wherein the third dimension represents an actual valueof the respective data point for the coordinates, and expanding thedisplay of a result of a genome-wide association study (GWAS) data setfor genome-wide variants including real-time replication of candidate orputative genes to limit statistical penalties in the visual array. 21.The method of claim 20, wherein the three or more output dimensions arefour output dimensions.
 22. The method of claim 20, wherein thenavigation is a three-dimensional moving video mode.
 23. A computerizedmethod of displaying large data sets, the method comprising: receivingan input data set, wherein the input data set comprises data that can beclassified in two classification dimensions; displaying the input dataset in a graph that have three or more output dimensions; and allowing auser to navigate above a plane of two output dimensions from the graph,and, wherein a baseline transversal axis of the graph is a list oftests, and a longitudinal axis of the graph lists ordered singlenucleotide polymorphisms (SNPs) to create a surface, and expanding thedisplay of a result of a genome-wide association study (GWAS) data setfor genome-wide variants including real-time replication of candidate orputative genes to limit statistical penalties in the visual array. 24.The method of claim 23, wherein a vertical axis rising out of thesurface represent log p-values, and wherein color reflects quantitativeassociation statistics.
 25. A computerized method of displaying largedata sets, the method comprising: receiving an input data set, whereinthe input data set comprises data that can be classified in twoclassification dimensions; displaying the input data set in a graph thathave three or more output dimensions; and allowing a user to navigateabove a plane of two output dimensions from the graph, and, wherein alarge data set is a genome-wide association studies (GWAS) or a wholegenome sequence (WGS) analysis, displaying contents of the large dataset as a visual array wherein two axes correspond to the coordinates anda third axis corresponds to a third dimension, wherein the thirddimension represents an actual value of the respective data point forthe coordinates, and expanding the display of a result of a genome-wideassociation study (GWAS) data set for genome-wide variants includingreal-time replication of candidate or putative genes to limitstatistical penalties in the visual array.
 26. The method of claim 1,wherein the second classification dimension is for grouping the inputdata into non-overlapping subsets.
 27. The method of claim 1, whereinthe second classification dimension represents analysis criteria, traitsof the data entries, or aspects of the data entries.
 28. The method ofclaim 27, wherein the second classification dimension representsanalysis criteria.
 29. The method of claim 27, wherein the secondclassification dimension represents traits of the data entries.
 30. Themethod of claim 27, wherein the second classification dimensionrepresents aspects of the data entries.
 31. The method of claim 27,wherein the second classification dimension represents differentassociation tests performed to generate the input data.